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         author = {Kleinert, Matthias and Helmke, Hartmut and Siol, Gerald and Ehr, heiko and Klakow, Dietrich and Singh, Mittul and Motlicek, Petr and Christian, Kern and Aneta, Cerna and Petr, Hlousek},
       projects = {Idiap, MALORCA},
          month = feb,
          title = {Adaptation of Assistant Based Speech Recognition to New Domains and Its Acceptance by Air Traffic Controllers},
      booktitle = {Proceedings of the 2nd International Conference on Intelligent Human Systems Integration (IHSI 2019): Integrating People and Intelligent Systems},
           year = {2019},
          pages = {820 - 826},
       location = {San Diego, California, USA},
            doi = {10.1007/978-3-030-11051-2_125},
       abstract = {In air traffic control rooms, paper flight strips are more and more replaced by digital solutions. The digital systems, however, increase the workload for air traffic controllers: For instance, each voice-command must be manually inserted into the system by the controller. Recently the AcListant{\textregistered} project has validated that Assistant Based Speech Recognition (ABSR) can replace the manual inputs by automatically recognized voice commands. Adaptation of ABSR to different environments, however, has shown to be expensive. The Horizon 2020 funded project MALORCA (MAchine Learning Of Speech Recognition Models for Controller Assistance), proposed a more effective adaptation solution integrating a machine learning framework. As a first showcase, ABSR was automatically adapted with radar data and voice recordings for Prague and Vienna. The system reaches command recognition error rates of 0.6\% (Prague) resp. 3.2\% (Vienna). This paper describes the feedback trials with controllers from Vienna and Prague.}